TAL effectors are a class of proteins used by bacterial pathogens of plants to directly modulate host gene expression. They bind to effector-specific host DNA sequences, and this specificity is governed by a one-to- one correspondence between a variable pair of residues in each repeat and nucleotides that make up the target DNA sequences. TAL effectors with new DNA sequence specificities can be created by assembling the appropriate repeats in the necessary order in the DNA binding domain. Further, the fusion of native or tailored TAL effector DNA binding domains to the restriction endonuclease FokI creates targeted double-strand DNA breaks at predicted sequences. These properties indicate that TAL effectors hold much promise as customizable DNA binding scaffolds for targeted gene regulation and genetic modification. To realize this potential, this project aims to 1) determine the basis for TAL effector DNA binding affinity and specificity, 2) enhance targeting capacity, and 3) implement and assess custom TAL effectors and TAL effector nucleases for genome manipulation. The first aim will be accomplished by determining the contributions of associations of individual TAL effector repeats with target nucleotides to overall binding affinity and specificity and whether the contributions are cooperative or additive. Further, biophysical and crystallographic structure analysis will be performed for distinct TAL effectors bound to their DNA targets. The second aim will be approached by incorporating repeats with novel residue pairs that have potential for enhanced nucleotide affinity and specificity and by making modifications outside the repeat region that are predicted to relax the requirement for thymine at position -1 of each binding site. Finally, aim 3 will be achieved by using TAL effectors as custom transcription factors and TALENs as reagents for gene disruption and gene editing to determine efficacy, efficiency, and fidelity. These experiments will be conducted in both Arabidopsis and human cells to determine breadth of applicability and to take advantage of the strengths of each model to assess off-target effects.